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Diverse proteomics-based strategies have been applied to saliva to quantitatively identify diagnostic and prognostic targets for oral cancer. Considering that these targets may be regulated by events that do not imply variation in protein abundance levels, we hypothesized that changes in protein conformation can be associated with diagnosis and prognosis, revealing biological processes and novel targets of clinical relevance. For this, we employed limited proteolysis-mass spectrometry in saliva samples to explore structural alterations, comparing the proteome of healthy control and oral squamous cell carcinoma (OSCC) patients with and without lymph node metastasis. Thirty-six proteins with potential structural rearrangements were associated with clinical patient features including transketolase and its interacting partners. Moreover, N -glycosylated peptides contribute to structural rearrangements of potential diagnostic and prognostic markers. Altogether, this approach utilizes saliva proteins to search for targets for diagnosing and prognosing oral cancer and can guide the discovery of potential regulated sites beyond protein-level abundance.

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP 3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP 3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP 3 MS Workflow adds improved computing of the MS 2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H] + ion deconvolution using fragmentation rules; chemical structural annotation against MS 2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP 3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

The selection of a suitable proteotypic peptide remains a challenge for designing a targeted quantitative proteomics assay. Although the criteria are well-established in the literature, the selection of these peptides is often performed in a subjective and time-consuming manner. Here, we have developed a practical and semiautomated workflow implemented in an open-source program named Typic. Typic is designed to run in a command line and a graphical interface to help selecting a list of proteotypic peptides for targeted quantitation. The tool combines the input data and downloads additional data from public repositories to produce a file per protein as output. Each output file includes relevant information to the selection of proteotypic peptides organized in a table, a colored ranking of peptides according to their potential value as targets for quantitation and auxiliary plots to assist users in the task of proteotypic peptides selection. Taken together, Typic leads to a practical and straightforward data extraction from multiple data sets, allowing the identification of most suitable proteotypic peptides based on established criteria, in an unbiased and standardized manner, ultimately leading to a more robust targeted proteomics assay.

Background: Prognostic tools developed to stratify critically ill patients in Intensive Care Units (ICUs), are critical to predict those with higher risk of mortality in the first hours of admission. This study aims to evaluate the performance of the pShock score in critically ill patients admitted to the ICU with SARS-CoV-2 infection., Methods: Prospective observational analytical cohort study conducted between January 2020 and March 2021 in four general ICUs in Salvador, Brazil. Descriptive statistics were used to characterize the cohort and a logistic regression, followed by cross-validation, were performed to calibrate the score. A ROC curve analysis was used to assess accuracy of the models analyzed., Results: Six hundred five adult ICU patients were included in the study. The median age was 63 (IQR: 49-74) years with a mortality rate of 33.2% (201 patients). The calibrated pShock-CoV score performed well in prediction of ICU mortality (AUC of 0.80 [95% Confidence Interval (CI): 0.77-0.83; p -value < 0.0001])., Conclusions: The pShock-CoV score demonstrated robust discriminatory capacity and may assist in targeting scarce ICU resources during the COVID-19 pandemic to those critically ill patients most likely to benefit., Competing Interests: Authors TC, IF, RM, MA, KA, and BA were employed by fellows from Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Carmo, Ferreira, Menezes, Pina, Oliveira, Telles, Machado, Aguiar, Caldas, Arriaga, Akrami, Filgueiras Filho and Andrade.)

Objective: To assess the Simplified Acute Physiology Score 3 (SAPS3) prognostic score performance across different body mass index categories., Methods: A retrospective cohort study in a general ICU in Brazil. A secondary analysis of medical records was performed with clinical and epidemiological data. Patients were stratified according to their body mass index (BMI) category, and a binary logistic regression was then performed to identify factors independently associated with mortality. SAPS3 accuracy was determined using the area under the receiver operating characteristics curve and the Hosmer-Lemeshow test. A modified Kaplan-Meyer plot was employed to evaluate death probability according to BMI. ICU mortality was evaluated as the primary outcome., Results: A total of 2,179 patients (mean age of 67.9 years and female predominance (53.1%)) were enrolled. SAPS3 was found accurate in all groups except in the underweight (AUC: 0.694 95% CI 0.616-0.773; HL = 0.042). The patients in the underweight group tended to be older, have longer hospital stay, have worse functional status, and have a higher value on prognostic scores. After the adjustments, no statistically significant difference between the BMI groups was noted in relation to mortality, except for the low weight that presented a likelihood of death of 3.50 (95% CI, 1.43-8.58, p = 0.006)., Conclusion: This research showed that SAPS3 had poor accuracy in predicting ICU mortality in underweight patients. This group was shown to be an independent risk factor for worse clinical outcomes., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Saliva is a biofluid that maintains the health of oral tissues and the homeostasis of oral microbiota. Studies have demonstrated that Oral squamous cell carcinoma (OSCC) patients have different salivary microbiota than healthy individuals. However, the relationship between these microbial differences and clinicopathological outcomes is still far from conclusive. Herein, we investigate the capability of using metagenomic and metaproteomic saliva profiles to distinguish between Control (C), OSCC without active lesion (L0), and OSCC with active lesion (L1) patients. The results show that there are significantly distinct taxonomies and functional changes in L1 patients compared to C and L0 patients, suggesting compositional modulation of the oral microbiome, as the relative abundances of Centipeda, Veillonella, and Gemella suggested by metagenomics are correlated with tumor size, clinical stage, and active lesion. Metagenomics results also demonstrated that poor overall patient survival is associated with a higher relative abundance of Stenophotromonas, Staphylococcus, Centipeda, Selenomonas, Alloscordovia, and Acitenobacter. Finally, compositional and functional differences in the saliva content by metaproteomics analysis can distinguish healthy individuals from OSCC patients. In summary, our study suggests that oral microbiota and their protein abundance have potential diagnosis and prognosis value for oral cancer patients. Further studies are necessary to understand the role of uniquely detected metaproteins in the microbiota of healthy and OSCC patients as well as the crosstalk between saliva host proteins and the oral microbiome present in OSCC., (Copyright © 2021. Published by Elsevier B.V.)

Aims: Evaluate the abundance of the selected targets, alpha-1-antitrypsin (A1AT) and macrophage migration inhibitory factor (MIF), and correlate these findings with the risk of developing severe oral mucositis (OM)., Materials and Methods: Head and neck squamous cell carcinoma (HNSCC) patients submitted to radiotherapy (RT) or chemoradiotherapy (CRT) were assessed. OM grade and pain were evaluated daily during treatment. Two protein targets, A1AT and MIF, were evaluated, using selected reaction monitoring-mass spectrometry (SRM-MS), in whole saliva, collected prior to oncologic treatment. The results obtained from the targeted proteomic analysis were correlated with OM clinical outcomes., Results: A total of 27 patients were included, of whom 21 (77.8%) had locally advanced disease (clinical stage III or IV). Most patients (70.4%) received CRT. OM grades 2 (40.8%) and 3 (33.3%) were the most prevalent during RT with a mean highest reported OM-related pain of 3.22 through the visual analogue scale (VAS). The abundance of A1AT and MIF correlated significantly with severe (grades 3 or 4, p < 0.02) compared with moderate-low (grades 1 or 2, p < 0.04) OM grade., Conclusions: There is a correlation between the abundance of salivary A1AT and MIF and oncologic treatment-induced OM. The correlation of MIF expression with severe OM appears to be compatible with its physiological pro-inflammatory role. These results open up great possibilities for the use of salivary MIF and A1AT levels as prognostic markers for effective therapeutic interventions, such as photobiomodulation therapy, patient-controlled analgesia, or personalized medicaments.

Background: Severity stratification scores developed in intensive care units (ICUs) are used in interventional studies to identify the most critically ill. Studies that evaluate accuracy of these scores in ICU patients admitted with pneumonia are lacking. This study aims to determine performance of severity scores as predictors of mortality in critically ill patients admitted with pneumonia., Methods: Prospective cohort study in a general ICU in Brazil. ICU severity scores (Simplified Acute Physiology Score 3 [SAPS 3] and Sepsis-Related Organ Failure Assessment [qSOFA]), prognostic scores of pneumonia (CURB-65 [confusion, urea, respiratory rate, blood pressure, age] and CRB-65 [confusion, respiratory rate, blood pressure, age]), and clinical and epidemiological variables in the first 6 hours of hospitalization were analyzed., Results: Two hundred patients were included between 2015 and 2018, with a median age of 81 years (interquartile range, 67-90 years) and female predominance (52%), primarily admitted from the emergency department (65%) with community-acquired pneumonia (CAP, 80.5%). SAPS 3, CURB-65, CRB-65,and qSOFA all exhibited poor performance in predicting mortality. Multivariate regression identified variables independently associated with mortality that were used to develop a novel pneumonia-specific ICU severity score (Pneumonia Shock score) that outperformed SAPS 3, CURB-65, and CRB-65. The Shock score was validated in an external multicenter cohort of critically ill patients admitted with CAP., Conclusions: We created a parsimonious score that accurately identifies patients with pneumonia at highest risk of ICU death. These findings are critical to accurately stratify patients with severe pneumonia in therapeutic trials that aim to reduce mortality., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Bacterial genome sequencing has revealed a vast number of novel biosynthetic gene clusters (BGC) with potential to produce bioactive natural products. However, the biosynthesis of secondary metabolites by bacteria is often silenced under laboratory conditions, limiting the controlled expression of natural products. Here we describe an integrated methodology for the construction and screening of an elicited and pre-fractionated library of marine bacteria. In this pilot study, chemical elicitors were evaluated to mimic the natural environment and to induce the expression of cryptic BGCs in deep-sea bacteria. By integrating high-resolution untargeted metabolomics with cheminformatics analyses, it was possible to visualize, mine, identify and map the chemical and biological space of the elicited bacterial metabolites. The results show that elicited bacterial metabolites correspond to ~45% of the compounds produced under laboratory conditions. In addition, the elicited chemical space is novel (~70% of the elicited compounds) or concentrated in the chemical space of drugs. Fractionation of the crude extracts further evidenced minor compounds (~90% of the collection) and the detection of biological activity. This pilot work pinpoints strategies for constructing and evaluating chemically diverse bacterial natural product libraries towards the identification of novel bacterial metabolites in natural product-based drug discovery pipelines.

Objective: This study aimed to assess the performance of a commonly used ICU severity score (SAPS3) and determine whether an alternative scoring system may be more accurate across all age strata., Methods: Retrospective cohort study in a general ICU in Brazil. A secondary analysis was performed with clinical and epidemiological data, present in the first 24 hours of unit admission. Then, a binary logistic regression, followed by cross-validation, was made to develop a novel prognostic tool. ICU mortality was the primary outcome evaluated., Results: A total of 3042 patients were included over the study period between August 2015 and July 2018 with a median age of 67 ± 18.4 years. SAPS3 performed fairly in prediction of ICU mortality, particularly in the 80 years or older subset. Multivariable regression identified variables independently associated with mortality that were used to develop the Age Calibrated ICU Score (ACIS) tool that performed similarly to SAPS3 across age categories, being slightly superior in the very elderly population (AUC 0.80 vs 0.72)., Conclusions: The ACIS offers a robust and simple tool to predict ICU mortality, particularly in an increasingly elderly critical care population., Competing Interests: The authors have declared that no competing interests exist

Background: The construction of a suffix array for a collection of strings is a fundamental task in Bioinformatics and in many other applications that process strings. Related data structures, as the Longest Common Prefix array, the Burrows-Wheeler transform, and the document array, are often needed to accompany the suffix array to efficiently solve a wide variety of problems. While several algorithms have been proposed to construct the suffix array for a single string, less emphasis has been put on algorithms to construct suffix arrays for string collections., Result: In this paper we introduce gsufsort, an open source software for constructing the suffix array and related data indexing structures for a string collection with N symbols in O ( N ) time. Our tool is written in ANSI/C and is based on the algorithm gSACA-K (Louza et al. in Theor Comput Sci 678:22-39, 2017), the fastest algorithm to construct suffix arrays for string collections. The tool supports large fasta, fastq and text files with multiple strings as input. Experiments have shown very good performance on different types of strings., Conclusions: gsufsort is a fast, portable, and lightweight tool for constructing the suffix array and additional data structures for string collections., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

Objective: ICU severity scores such as the Sequential Organ Failure Assessment (SOFA) determine neurologic dysfunction based on the Glasgow Coma Scale, a tool that may be limited in a critically ill population. It remains unknown whether alternative methods to assess for neurologic dysfunction, such as FOUR and RASS, are superior. This study aimed to determine the predictive performance of a modified SOFA tool in a large Brazilian ICU cohort., Design: Prospective cohort single center study., Setting: Mixed surgical and medical ICU in Salvador, Bahia, Brazil between August 2015 and December 2018., Patients: All acutely ill ICU admissions, other than postoperative patients or those with insufficient data, were eligible for study inclusion., Measurements and Main Results: 2147 patients were admitted to the ICU, of which 999 meeting inclusion criteria were included in the final analysis with a median age of 72 years (IQR 58-83) and a female predominance 545 (54%). The SOFA score using GCS, RASS and FOUR for the neurologic component performed marginally in the ability to predict general ICU mortality (SOFAGCS AUC 0.74 vs SOFARASS AUC 0.71 and SOFAFOUR AUC 0.67), with SOFAFOUR performing significantly lower compared to either SOFARASS and SOFAGCS (p<0.04, p<0.004 respectively). All three scores demonstrated decreased discriminate function in the mechanically ventilated population (SOFAGCS AUC 0.70 vs SOFARASS AUC 0.70 and SOFAFOUR AUC 0.55), though SOFAFOUR remained significantly worse when compared to SOFAGCS or SOFARASS (p = 0.034, p = 0.014, respectively).. Furthermore, performance was poor in a subset of patients with sepsis (n = 145) at time of admission (SOFAGCS AUC 0.66 vs SOFARASS AUC 0.55 and SOFAFOUR AUC 0.56)., Conclusion: Modification of the neurologic component in the SOFA score does not appear to improve mortality prediction in the ICU., Competing Interests: The authors have declared that no competing interests exist.

Background: In Citrus cultures, three species of Xanthomonas are known to cause distinct diseases. X. citri subsp. citri patothype A, X. fuscans subsp. aurantifolii pathotypes B and C, and X. alfalfae subsp. citrumelonis , are the causative agents of cancrosis A, B, C, and citrus bacterial spots, respectively. Although these species exhibit different levels of virulence and aggressiveness, only limited alternatives are currently available for proper and early detection of these diseases in the fields. The present study aimed to develop a new molecular diagnostic method based on genomic sequences derived from the four species of Xanthomonas ., Results: Using comparative genomics approaches, primers were synthesized for the identification of the four causative agents of citrus diseases. These primers were validated for their specificity to their target DNA by both conventional and multiplex PCR. Upon evaluation, their sensitivity was found to be 0.02 ng/µl in vitro and 1.5 × 10 4 CFU ml -1 in infected leaves. Additionally, none of the primers were able to generate amplicons in 19 other genomes of Xanthomonas not associated with Citrus and one species of Xylella , the causal agent of citrus variegated chlorosis (CVC). This denotes strong specificity of the primers for the different species of Xanthomonas investigated in this study., Conclusions: We demonstrated that these markers can be used as potential candidates for performing in vivo molecular diagnosis exclusively for citrus-associated Xanthomonas . The bioinformatics pipeline developed in this study to design specific genomic regions is capable of generating specific primers. It is freely available and can be utilized for any other model organism., Competing Interests: João C. Setubal is an Academic Editor for PeerJ. Commercial use of the oligonucleotides and amplicons generated by PCR in this work is protected by Brazillian patent application number BR 10 2018 067332 7., (©2019 Fonseca et al.)

Background: Sequencing technologies produce larger and larger collections of biosequences that have to be stored in compressed indices supporting fast search operations. Many compressed indices are based on the Burrows-Wheeler Transform (BWT) and the longest common prefix (LCP) array. Because of the sheer size of the input it is important to build these data structures in external memory and time using in the best possible way the available RAM., Results: We propose a space-efficient algorithm to compute the BWT and LCP array for a collection of sequences in the external or semi-external memory setting. Our algorithm splits the input collection into subcollections sufficiently small that it can compute their BWT in RAM using an optimal linear time algorithm. Next, it merges the partial BWTs in external or semi-external memory and in the process it also computes the LCP values. Our algorithm can be modified to output two additional arrays that, combined with the BWT and LCP array, provide simple, scan-based, external memory algorithms for three well known problems in bioinformatics: the computation of maximal repeats, the all pairs suffix-prefix overlaps, and the construction of succinct de Bruijn graphs., Conclusions: We prove that our algorithm performs O ( n maxlcp ) sequential I/Os, where n is the total length of the collection and maxlcp is the maximum LCP value. The experimental results show that our algorithm is only slightly slower than the state of the art for short sequences but it is up to 40 times faster for longer sequences or when the available RAM is at least equal to the size of the input.

Different regions of oral squamous cell carcinoma (OSCC) have particular histopathological and molecular characteristics limiting the standard tumor-node-metastasis prognosis classification. Therefore, defining biological signatures that allow assessing the prognostic outcomes for OSCC patients would be of great clinical significance. Using histopathology-guided discovery proteomics, we analyze neoplastic islands and stroma from the invasive tumor front (ITF) and inner tumor to identify differentially expressed proteins. Potential signature proteins are prioritized and further investigated by immunohistochemistry (IHC) and targeted proteomics. IHC indicates low expression of cystatin-B in neoplastic islands from the ITF as an independent marker for local recurrence. Targeted proteomics analysis of the prioritized proteins in saliva, combined with machine-learning methods, highlights a peptide-based signature as the most powerful predictor to distinguish patients with and without lymph node metastasis. In summary, we identify a robust signature, which may enhance prognostic decisions in OSCC and better guide treatment to reduce tumor recurrence or lymph node metastasis.

Background: In phylogenetic reconstruction the result is a tree where all taxa are leaves and internal nodes are hypothetical ancestors. In a live phylogeny, both ancestral and living taxa may coexist, leading to a tree where internal nodes may be living taxa. The well-known Neighbor-Joining heuristic is largely used for phylogenetic reconstruction., Results: We present Live Neighbor-Joining, a heuristic for building a live phylogeny. We have investigated Live Neighbor-Joining on datasets of viral genomes, a plausible scenario for its application, which allowed the construction of alternative hypothesis for the relationships among virus that embrace both ancestral and descending taxa. We also applied Live Neighbor-Joining on a set of bacterial genomes and to sets of images and texts. Non-biological data may be better explored visually when their relationship in terms of content similarity is represented by means of a phylogeny., Conclusion: Our experiments have shown interesting alternative phylogenetic hypothesis for RNA virus genomes, bacterial genomes and alternative relationships among images and texts, illustrating a wide range of scenarios where Live Neighbor-Joining may be used.

Background: Suffix arrays, augmented by additional data structures, allow solving efficiently many string processing problems. The external memory construction of the generalized suffix array for a string collection is a fundamental task when the size of the input collection or the data structure exceeds the available internal memory., Results: In this article we present and analyze [Formula: see text] [introduced in CPM (External memory generalized suffix and [Formula: see text] arrays construction. In: Proceedings of CPM. pp 201-10, 2013)], the first external memory algorithm to construct generalized suffix arrays augmented with the longest common prefix array for a string collection. Our algorithm relies on a combination of buffers, induced sorting and a heap to avoid direct string comparisons. We performed experiments that covered different aspects of our algorithm, including running time, efficiency, external memory access, internal phases and the influence of different optimization strategies. On real datasets of size up to 24 GB and using 2 GB of internal memory, [Formula: see text] showed a competitive performance when compared to [Formula: see text] and [Formula: see text], which are efficient algorithms for a single string according to the related literature. We also show the effect of disk caching managed by the operating system on our algorithm., Conclusions: The proposed algorithm was validated through performance tests using real datasets from different domains, in various combinations, and showed a competitive performance. Our algorithm can also construct the generalized Burrows-Wheeler transform of a string collection with no additional cost except by the output time.

Background: The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability., Results: Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB., Conclusions: CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.

Background: Set comparisons permeate a large number of data analysis workflows, in particular workflows in biological sciences. Venn diagrams are frequently employed for such analysis but current tools are limited., Results: We have developed InteractiVenn, a more flexible tool for interacting with Venn diagrams including up to six sets. It offers a clean interface for Venn diagram construction and enables analysis of set unions while preserving the shape of the diagram. Set unions are useful to reveal differences and similarities among sets and may be guided in our tool by a tree or by a list of set unions. The tool also allows obtaining subsets' elements, saving and loading sets for further analyses, and exporting the diagram in vector and image formats. InteractiVenn has been used to analyze two biological datasets, but it may serve set analysis in a broad range of domains., Conclusions: InteractiVenn allows set unions in Venn diagrams to be explored thoroughly, by consequence extending the ability to analyze combinations of sets with additional observations, yielded by novel interactions between joined sets. InteractiVenn is freely available online at: www.interactivenn.net .

A Bacillus cereus strain, FT9, isolated from a hot spring in the midwest region of Brazil, had its entire genome sequenced., (Copyright © 2014 Raiol et al.)

Composting operations are a rich source for prospection of biomass degradation enzymes. We have analyzed the microbiomes of two composting samples collected in a facility inside the São Paulo Zoo Park, in Brazil. All organic waste produced in the park is processed in this facility, at a rate of four tons/day. Total DNA was extracted and sequenced with Roche/454 technology, generating about 3 million reads per sample. To our knowledge this work is the first report of a composting whole-microbial community using high-throughput sequencing and analysis. The phylogenetic profiles of the two microbiomes analyzed are quite different, with a clear dominance of members of the Lactobacillus genus in one of them. We found a general agreement of the distribution of functional categories in the Zoo compost metagenomes compared with seven selected public metagenomes of biomass deconstruction environments, indicating the potential for different bacterial communities to provide alternative mechanisms for the same functional purposes. Our results indicate that biomass degradation in this composting process, including deconstruction of recalcitrant lignocellulose, is fully performed by bacterial enzymes, most likely by members of the Clostridiales and Actinomycetales orders.

The live phylogeny problem generalizes the phylogeny problem while admitting the existence of living ancestors among the taxonomic objects. This problem suits the case of fast-evolving species, like virus, and the construction of phylogenies for nonbiological objects like documents, images, and database records. In this article, we formalize the live phylogeny problem for distances and character states and introduce polynomial-time algorithms for particular versions of the problems. We believe that more general versions of the problems are NP-hard and that many heuristic and approximation approaches may be developed as solution strategies.

An alternative form to multidimensional projections for the visual analysis of data represented in multidimensional spaces is the deployment of similarity trees, such as Neighbor Joining trees. They organize data objects on the visual plane emphasizing their levels of similarity with high capability of detecting and separating groups and subgroups of objects. Besides this similarity-based hierarchical data organization, some of their advantages include the ability to decrease point clutter; high precision; and a consistent view of the data set during focusing, offering a very intuitive way to view the general structure of the data set as well as to drill down to groups and subgroups of interest. Disadvantages of similarity trees based on neighbor joining strategies include their computational cost and the presence of virtual nodes that utilize too much of the visual space. This paper presents a highly improved version of the similarity tree technique. The improvements in the technique are given by two procedures. The first is a strategy that replaces virtual nodes by promoting real leaf nodes to their place, saving large portions of space in the display and maintaining the expressiveness and precision of the technique. The second improvement is an implementation that significantly accelerates the algorithm, impacting its use for larger data sets. We also illustrate the applicability of the technique in visual data mining, showing its advantages to support visual classification of data sets, with special attention to the case of image classification. We demonstrate the capabilities of the tree for analysis and iterative manipulation and employ those capabilities to support evolving to a satisfactory data organization and classification., (© 2011 IEEE)

To contribute to our understanding of the genome complexity of sugarcane, we undertook a large-scale expressed sequence tag (EST) program. More than 260,000 cDNA clones were partially sequenced from 26 standard cDNA libraries generated from different sugarcane tissues. After the processing of the sequences, 237,954 high-quality ESTs were identified. These ESTs were assembled into 43,141 putative transcripts. Of the assembled sequences, 35.6% presented no matches with existing sequences in public databases. A global analysis of the whole SUCEST data set indicated that 14,409 assembled sequences (33% of the total) contained at least one cDNA clone with a full-length insert. Annotation of the 43,141 assembled sequences associated almost 50% of the putative identified sugarcane genes with protein metabolism, cellular communication/signal transduction, bioenergetics, and stress responses. Inspection of the translated assembled sequences for conserved protein domains revealed 40,821 amino acid sequences with 1415 Pfam domains. Reassembling the consensus sequences of the 43,141 transcripts revealed a 22% redundancy in the first assembling. This indicated that possibly 33,620 unique genes had been identified and indicated that >90% of the sugarcane expressed genes were tagged.